CV Wizard: Does a Clinical Decision Support Tool Improve CVD Risk Factor Control in Safety Net Clinics?
NCT ID: NCT03001713
Last Updated: 2025-02-11
Study Results
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View full resultsBasic Information
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COMPLETED
NA
70 participants
INTERVENTIONAL
2018-02-22
2024-02-28
Brief Summary
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Detailed Description
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Our overarching aims are to:
Aim 1. Conduct a clinic-randomized trial of the impact of an evidence-based point-of-care CDS system on (i) overall CVD risk scores, and (ii) control of individual CVD risks (blood pressure; HbA1c, lipid levels; aspirin use; smoking; body mass index), among high CVD risk CHC adult patients. H1: High CVD risk patients in Arm 1 CHCs will have significantly lower overall CVD risk scores over a 12-month post-index visit period, compared to those in Arm 2 CHCs. H2: High CVD risk patients in Arm 1 CHCs who have poor control of specific CVD risk factors at an index visit will have significantly better control of those factors over a 12 month post-index visit period, compared to those in Arm 2 CHCs. H3: Disparities in specific CVD risk factor control between CHC patients' versus national CVD risk factor control rates will be significantly reduced by 18 months post-implementation in each Arm (secondary analysis).
Aim 2. Develop and hone need-based implementation support protocols to help Arm 1 CHCs implement the CV Wizard CDS system into their standard workflows; assess whether use of the protocols developed for Arm 1 CHCs accelerates implementation and adoption of the CDS system in the Arm 2 CHCs. H4: CDS uptake into CHC workflows will be significantly faster in Arm 2 CHCs than in Arm 1 CHCs.
Aim 3. Conduct a mixed methods process evaluation, guided by the Technology Acceptance Model, to identify and address patient, provider, and delivery system barriers to uptake / impact of this CDS in CHCs.
Conditions
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Study Design
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RANDOMIZED
PARALLEL
HEALTH_SERVICES_RESEARCH
NONE
Study Groups
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Arm 1: Immediate implementation
30 safety net community health centers (CHCs) will be randomized to implement the sophisticated CV Wizard clinical decision support (CDS) system at the start of study year 2. Arm 1 CHCs will receive implementation support that will be pragmatically iterated to address any barriers to adoption / sustained use of the CDS that are identified through study activities. The investigators will apply these learnings to improve adoption rates in Arm 2.
CV WIZARD
This project will determine whether a sophisticated CDS system will be effective in CHCs. The innovative, point-of-care, web-based CDS system we will test ("CV Wizard") generates a guideline-based prioritized summary of each patient's major CVD risk factors, then presents patient and provider 'views' of this summary, with individualized care recommendations.
Arm 2: Delayed implementation
30 safety net community health centers (CHCs) will be randomized to implement the sophisticated CV Wizard clinical decision support (CDS) system 18 months later than Arm 1. The investigators will measure the intervention's impact on CVD risk factor control in CHCs.
CV WIZARD
This project will determine whether a sophisticated CDS system will be effective in CHCs. The innovative, point-of-care, web-based CDS system we will test ("CV Wizard") generates a guideline-based prioritized summary of each patient's major CVD risk factors, then presents patient and provider 'views' of this summary, with individualized care recommendations.
Interventions
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CV WIZARD
This project will determine whether a sophisticated CDS system will be effective in CHCs. The innovative, point-of-care, web-based CDS system we will test ("CV Wizard") generates a guideline-based prioritized summary of each patient's major CVD risk factors, then presents patient and provider 'views' of this summary, with individualized care recommendations.
Eligibility Criteria
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Inclusion Criteria
* Persons aged 18-21 with high-CVD risk
* Some subjects with mental health conditions of various types
Exclusion Criteria
Note: The investigators are not enrolling patients for this clinic-randomized study, but rather studying the uptake and impact of a set of EHR-based clinical decision support tools into regular care at the participating clinics. In this clinic-randomized trial, the intervention / randomization are clinic level.
18 Years
ALL
Yes
Sponsors
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OCHIN, Inc.
OTHER
HealthPartners Institute
OTHER
Kaiser Permanente
OTHER
Responsible Party
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Principal Investigators
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Rachel Gold, PhD, MPH
Role: PRINCIPAL_INVESTIGATOR
Kaiser Permanente
Locations
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Kaiser Permanente Center for Health Research
Portland, Oregon, United States
Countries
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References
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Boston D, Larson AE, Sheppler CR, O'Connor PJ, Sperl-Hillen JM, Hauschildt J, Gold R. Does Clinical Decision Support Increase Appropriate Medication Prescribing for Cardiovascular Risk Reduction? J Am Board Fam Med. 2023 Oct 11;36(5):777-788. doi: 10.3122/jabfm.2022.220391R2. Epub 2023 Sep 13.
Hauschildt J, Lyon-Scott K, Sheppler CR, Larson AE, McMullen C, Boston D, O'Connor PJ, Sperl-Hillen JM, Gold R. Adoption of shared decision-making and clinical decision support for reducing cardiovascular disease risk in community health centers. JAMIA Open. 2023 Mar 10;6(1):ooad012. doi: 10.1093/jamiaopen/ooad012. eCollection 2023 Apr.
Gold R, Larson AE, Sperl-Hillen JM, Boston D, Sheppler CR, Heintzman J, McMullen C, Middendorf M, Appana D, Thirumalai V, Romer A, Bava J, Davis JV, Yosuf N, Hauschildt J, Scott K, Moore S, O'Connor PJ. Effect of Clinical Decision Support at Community Health Centers on the Risk of Cardiovascular Disease: A Cluster Randomized Clinical Trial. JAMA Netw Open. 2022 Feb 1;5(2):e2146519. doi: 10.1001/jamanetworkopen.2021.46519.
Provided Documents
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Document Type: Study Protocol and Statistical Analysis Plan
Other Identifiers
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HL133793-01A1
Identifier Type: -
Identifier Source: org_study_id
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